Truthful mechanisms for agents that value privacy

Chong, Stephen; Kash, Ian A.; Moran, Tal; Vadhan, Salil

doi:10.1145/2482540.2482549

Cited by 43 publications

(58 citation statements)

References 20 publications

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“…Generalization involves replacing specific values with more general categories. For example, we might replace an exact age (say, 35) with a 10-year range (30)(31)(32)(33)(34)(35)(36)(37)(38)(39). (In an interesting coincidence, this type of discretization was also used to reduce information asymmetries in the diamond market-see Section 2.2.)…”

Section: Initial Attempts To Pin Down "Privacy": K-anonymity and Quermentioning

confidence: 99%

“…Although none of these works directly addresses issues with regulatory data, they provide strong connections between privacy and game-theoretic microeconomic questions. For example, several works use differential privacy to design incentive-compatible mechanisms for auction and allocation problems, starting with Mc-Sherry and Talwar [149] (see also [205,38,163,164,112]). A different line of work uses differential privacy for equilibrium selection [123].…”

Section: Some Applicationsmentioning

confidence: 99%

“…Since the recent crisis, a number of financial regulators have initiated the publication of indexes of financial conditions. Prominent examples include the four new financial stress indexes now published by various Federal Reserve Banks [169,[38][39][40][41][42][43][44][45], an index for Canada [113], and indexes that the IMF has constructed for various countries. All these measures aim to condense the state of an enormously complex financial system into a single number that can be tracked through time.…”

Section: Introductionmentioning

confidence: 99%

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Cryptography and the Economics of Supervisory Information: Balancing Transparency and Confidentiality

Flood¹,

Katz²,

Ong

et al. 2013

Working Paper (Federal Reserve Bank of Cleveland)

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We elucidate the tradeoffs between transparency and confi dentiality in the context of fi nancial regulation. The structure of information in fi nancial contexts creates incentives with a pervasive effect on fi nancial institutions and their relationships. This includes supervisory institutions, which must balance the opposing forces of confi dentiality and transparency that arise from their examination and disclosure duties. Prudential supervision can expose confi dential information to examiners who have a duty to protect it. Disclosure policies work to reduce information asymmetries, empowering investors and fostering market discipline. The resulting confi dentiality/transparency dichotomy tends to push supervisory information policies to one extreme or the other. We argue that there are important intermediate cases in which limited information sharing would be welfare-improving, and that this can be achieved with careful use of new techniques from the fi elds of secure computation and statistical data privacy. We provide a broad overview of these new technologies. We also describe three specifi c usage scenarios where such benefi cial solutions might be implemented.

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Section: Initial Attempts To Pin Down "Privacy": K-anonymity and Quermentioning

confidence: 99%

Section: Some Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cryptography and the Economics of Supervisory Information: Balancing Transparency and Confidentiality

Flood¹,

Katz²,

Ong

et al. 2013

Working Paper (Federal Reserve Bank of Cleveland)

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“…In [22], authors provides a generic transformation of any truthful mechanism into one that is differentially private, but truthfulness breaks if privacy itself influences utility. Several other papers provide mechanisms that are both truthful and private [3,15,17]. In their mechanisms, data subject utility depends on the output of the mechanism as well as privacy, so the mechanism itself may provide value to the data subjects.…”

Section: Related Workmentioning

confidence: 99%

Choosing Epsilon for Privacy as a Service

Krehbiel

2018

Proceedings on Privacy Enhancing Technologies

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In many real world scenarios, terms of service allow a producer of a service to collect data from its users. Producers value data but often only compensate users for their data indirectly with reduced prices for the service. This work considers how a producer (data analyst) may offer differential privacy as a premium service for its users (data subjects), where the degree of privacy offered may itself depend on the user data. Along the way, it strengthens prior negative results for privacy markets to the pay-for-privacy setting and develops a new notion of endogenous differential privacy. A positive result for endogenous privacy is given in the form of a class of mechanisms for privacy-as-a-service markets that 1) determine ɛ using the privacy and accuracy preferences of a heterogeneous body of data subjects and a single analyst, 2) collect and distribute payments for the chosen level of privacy, and 3) privately analyze the database. These mechanisms are endogenously differentially private with respect to data subjects’ privacy preferences as well as their private data, they directly elicit data subjects’ true preferences, and they determine a level of privacy that is efficient given all parties’ preferences.

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“…A mechanism (or algorithm) is differentially private, if its output is insensitive to the change of a single input. As Chen et al (2013) note, differential privacy is a property of the algorithm and there is a trade-off between privacy and the accuracy of statistics computed from the obtained data.…”

Section: Differential Privacymentioning

confidence: 99%

State-of-the-Art of the Economics of Cyber-Security and Privacy

Jentzsch

2016

SSRN Journal

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Box 1 Challenges in Estimation of Risk Metrics __________________________________________ Box 2 Important Standards in the PACS Domain ________________________________________ Box 3 The Economics of Big Data Analysis _____________________________________________ Box 4 Information Externalities and Pricing ____________________________________________ IPACSO-A Coordination Action under the FP7 DG CNECT Trustworthy ICT Program © IPACSO 2 IPACSO D4.1-p. 5 Preface IPACSO is an EU-funded CoOrdination and Support Action (CSA) project aimed at supporting Privacy and Cyber-security innovations in Europe. The key aim is to support ICT Security innovators with state of the art innovation methodologies and best practices that improve their overall innovation process. IPACSO will focus on adapting existing innovation methodologies available in other domains, both general and specific, optimizing these approaches for the Privacy and Cyber-Security (PACS) market domains. Ultimately, IPACSO will combine innovation support modules based on establish methods (both generic and technology-specific), with new innovation support approaches geared toward the specific needs of the European PACS marketplace. Market information of high-relevance to PACS innovators will also be included in the project. From an outreach perspective, innovators working alongside IPACSO will be able to increase their understanding of existing methodologies, best practices, market considerations, economic incentives and share key opinions alongside other peer experts in the PACS domain. By reaching out to IPACSO, privacy and cyber-security innovators will be put in touch with other innovators, business support organizations and financing. With IPACSO, privacy and cyber-security innovators will be getting additional promotion for their innovations, as IPACSO specifically focuses on innovations. The following scheme outlines in brief the relation of the two deliverables D4.1 and D4.2 to the developed Privacy and Cyber-Security Market Scheme. It enables the reader to see what is covered in the different deliverables and to what deliverable he/she needs to turn in order to find the information of interest. Cyber-Security Market Scheme and IPACSO Deliverable IPACSO-A Coordination Action under the FP7 DG CNECT Trustworthy ICT Program © IPACSO 2 IPACSO D4.1-p. 6

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Truthful mechanisms for agents that value privacy

Cited by 43 publications

References 20 publications

Cryptography and the Economics of Supervisory Information: Balancing Transparency and Confidentiality

Cryptography and the Economics of Supervisory Information: Balancing Transparency and Confidentiality

Choosing Epsilon for Privacy as a Service

State-of-the-Art of the Economics of Cyber-Security and Privacy

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